Overview

Brought to you by YData

Dataset statistics

Number of variables44
Number of observations873
Missing cells152
Missing cells (%)0.4%
Duplicate rows2
Duplicate rows (%)0.2%
Total size in memory300.2 KiB
Average record size in memory352.2 B

Variable types

Numeric5
Categorical36
DateTime3

Alerts

Dataset has 2 (0.2%) duplicate rowsDuplicates
max_energy is highly overall correlated with shock_countHigh correlation
rhythm_Asystole is highly overall correlated with rhythm_nanHigh correlation
rhythm_VF is highly overall correlated with shockable_rhythmHigh correlation
rhythm_VT is highly overall correlated with shockable_rhythmHigh correlation
rhythm_nan is highly overall correlated with rhythm_AsystoleHigh correlation
shock_count is highly overall correlated with max_energyHigh correlation
shockable_rhythm is highly overall correlated with rhythm_VF and 1 other fieldsHigh correlation
survival_24h is highly overall correlated with survival_to_dischargeHigh correlation
survival_to_discharge is highly overall correlated with survival_24hHigh correlation
coronary_artery_disease is highly imbalanced (87.7%)Imbalance
copd is highly imbalanced (53.2%)Imbalance
covid_on_admission is highly imbalanced (61.8%)Imbalance
shockable_rhythm is highly imbalanced (89.5%)Imbalance
survival_24h is highly imbalanced (51.3%)Imbalance
survival_to_discharge is highly imbalanced (79.5%)Imbalance
rhythm_AF is highly imbalanced (96.7%)Imbalance
rhythm_Bradycardia is highly imbalanced (55.1%)Imbalance
rhythm_PEA is highly imbalanced (93.3%)Imbalance
rhythm_Sinus/Other is highly imbalanced (90.2%)Imbalance
rhythm_VF is highly imbalanced (92.5%)Imbalance
rhythm_VT is highly imbalanced (95.8%)Imbalance
loc_CCU is highly imbalanced (83.0%)Imbalance
loc_COVID_WARD is highly imbalanced (91.0%)Imbalance
loc_CVU is highly imbalanced (63.0%)Imbalance
loc_DSU is highly imbalanced (91.0%)Imbalance
loc_FMS is highly imbalanced (75.7%)Imbalance
loc_MMS is highly imbalanced (60.1%)Imbalance
loc_NEONATAL_ICU is highly imbalanced (98.7%)Imbalance
loc_OBSTETRICS is highly imbalanced (98.7%)Imbalance
loc_OPERATING_ROOM is highly imbalanced (97.7%)Imbalance
loc_PEDIATRIC is highly imbalanced (89.5%)Imbalance
loc_PEDIATRIC_ICU is highly imbalanced (97.7%)Imbalance
loc_POST_ICU is highly imbalanced (94.1%)Imbalance
loc_UNKNOWN is highly imbalanced (98.7%)Imbalance
cpr_duration_min has 71 (8.1%) missing valuesMissing
rosc_dt has 70 (8.0%) missing valuesMissing
arrest_to_cpr_min is highly skewed (γ1 = 29.46183923)Skewed
shock_count has 830 (95.1%) zerosZeros
max_energy has 830 (95.1%) zerosZeros
cpr_duration_min has 21 (2.4%) zerosZeros
arrest_to_cpr_min has 850 (97.4%) zerosZeros

Reproduction

Analysis started2025-11-14 16:26:55.699750
Analysis finished2025-11-14 16:27:06.143501
Duration10.44 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct89
Distinct (%)10.2%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean71.147936
Minimum0
Maximum113
Zeros4
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2025-11-14T16:27:06.248822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.55
Q164
median76
Q385
95-th percentile91
Maximum113
Range113
Interquartile range (IQR)21

Descriptive statistics

Standard deviation19.96495
Coefficient of variation (CV)0.28061179
Kurtosis3.0699422
Mean71.147936
Median Absolute Deviation (MAD)10
Skewness-1.6905547
Sum62041
Variance398.59922
MonotonicityNot monotonic
2025-11-14T16:27:06.386882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8242
 
4.8%
8634
 
3.9%
8333
 
3.8%
9132
 
3.7%
7932
 
3.7%
8031
 
3.6%
8829
 
3.3%
7125
 
2.9%
7025
 
2.9%
8924
 
2.7%
Other values (79)565
64.7%
ValueCountFrequency (%)
04
 
0.5%
111
1.3%
25
0.6%
33
 
0.3%
71
 
0.1%
91
 
0.1%
101
 
0.1%
132
 
0.2%
153
 
0.3%
161
 
0.1%
ValueCountFrequency (%)
1131
 
0.1%
1011
 
0.1%
991
 
0.1%
981
 
0.1%
972
 
0.2%
962
 
0.2%
951
 
0.1%
9416
1.8%
934
 
0.5%
9212
1.4%

gender
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
1
445 
0
427 
-1
 
1

Length

Max length2
Median length1
Mean length1.0011455
Min length1

Characters and Unicode

Total characters874
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1445
51.0%
0427
48.9%
-11
 
0.1%

Length

2025-11-14T16:27:06.501673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:06.569629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1446
51.1%
0427
48.9%

Most occurring characters

ValueCountFrequency (%)
1446
51.0%
0427
48.9%
-1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1446
51.0%
0427
48.9%
-1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1446
51.0%
0427
48.9%
-1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1446
51.0%
0427
48.9%
-1
 
0.1%

smoking
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
665 
1
196 
-1
 
12

Length

Max length2
Median length1
Mean length1.0137457
Min length1

Characters and Unicode

Total characters885
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0665
76.2%
1196
 
22.5%
-112
 
1.4%

Length

2025-11-14T16:27:06.652069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:06.720643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0665
76.2%
1208
 
23.8%

Most occurring characters

ValueCountFrequency (%)
0665
75.1%
1208
 
23.5%
-12
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)885
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0665
75.1%
1208
 
23.5%
-12
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)885
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0665
75.1%
1208
 
23.5%
-12
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)885
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0665
75.1%
1208
 
23.5%
-12
 
1.4%

coronary_artery_disease
Categorical

Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
848 
1
 
24
-1
 
1

Length

Max length2
Median length1
Mean length1.0011455
Min length1

Characters and Unicode

Total characters874
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0848
97.1%
124
 
2.7%
-11
 
0.1%

Length

2025-11-14T16:27:06.822025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:06.887324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0848
97.1%
125
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0848
97.0%
125
 
2.9%
-1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0848
97.0%
125
 
2.9%
-1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0848
97.0%
125
 
2.9%
-1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0848
97.0%
125
 
2.9%
-1
 
0.1%

heart_failure
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
699 
1
174 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0699
80.1%
1174
 
19.9%

Length

2025-11-14T16:27:07.207240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:07.268394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0699
80.1%
1174
 
19.9%

Most occurring characters

ValueCountFrequency (%)
0699
80.1%
1174
 
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0699
80.1%
1174
 
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0699
80.1%
1174
 
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0699
80.1%
1174
 
19.9%

heart_disease
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
1
491 
0
382 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1491
56.2%
0382
43.8%

Length

2025-11-14T16:27:07.347175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:07.409872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1491
56.2%
0382
43.8%

Most occurring characters

ValueCountFrequency (%)
1491
56.2%
0382
43.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1491
56.2%
0382
43.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1491
56.2%
0382
43.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1491
56.2%
0382
43.8%

hypertension
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
1
535 
0
337 
-1
 
1

Length

Max length2
Median length1
Mean length1.0011455
Min length1

Characters and Unicode

Total characters874
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1535
61.3%
0337
38.6%
-11
 
0.1%

Length

2025-11-14T16:27:07.488131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:07.555720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1536
61.4%
0337
38.6%

Most occurring characters

ValueCountFrequency (%)
1536
61.3%
0337
38.6%
-1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1536
61.3%
0337
38.6%
-1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1536
61.3%
0337
38.6%
-1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1536
61.3%
0337
38.6%
-1
 
0.1%

copd
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
786 
1
87 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0786
90.0%
187
 
10.0%

Length

2025-11-14T16:27:07.641361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:07.704576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0786
90.0%
187
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0786
90.0%
187
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0786
90.0%
187
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0786
90.0%
187
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0786
90.0%
187
 
10.0%

diabetes
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
478 
1
394 
-1
 
1

Length

Max length2
Median length1
Mean length1.0011455
Min length1

Characters and Unicode

Total characters874
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0478
54.8%
1394
45.1%
-11
 
0.1%

Length

2025-11-14T16:27:07.792862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:07.868764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0478
54.8%
1395
45.2%

Most occurring characters

ValueCountFrequency (%)
0478
54.7%
1395
45.2%
-1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0478
54.7%
1395
45.2%
-1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0478
54.7%
1395
45.2%
-1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0478
54.7%
1395
45.2%
-1
 
0.1%

cancer
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
752 
1
121 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0752
86.1%
1121
 
13.9%

Length

2025-11-14T16:27:07.950886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:08.020128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0752
86.1%
1121
 
13.9%

Most occurring characters

ValueCountFrequency (%)
0752
86.1%
1121
 
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0752
86.1%
1121
 
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0752
86.1%
1121
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0752
86.1%
1121
 
13.9%

covid_on_admission
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
808 
1
 
65

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0808
92.6%
165
 
7.4%

Length

2025-11-14T16:27:08.095728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:08.153274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0808
92.6%
165
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0808
92.6%
165
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0808
92.6%
165
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0808
92.6%
165
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0808
92.6%
165
 
7.4%

shock_count
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.098510882
Minimum0
Maximum9
Zeros830
Zeros (%)95.1%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2025-11-14T16:27:08.211659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.55801917
Coefficient of variation (CV)5.6645434
Kurtosis104.01511
Mean0.098510882
Median Absolute Deviation (MAD)0
Skewness8.8834649
Sum86
Variance0.31138539
MonotonicityNot monotonic
2025-11-14T16:27:08.288698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0830
95.1%
122
 
2.5%
212
 
1.4%
34
 
0.5%
42
 
0.2%
91
 
0.1%
51
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
0830
95.1%
122
 
2.5%
212
 
1.4%
34
 
0.5%
42
 
0.2%
51
 
0.1%
61
 
0.1%
91
 
0.1%
ValueCountFrequency (%)
91
 
0.1%
61
 
0.1%
51
 
0.1%
42
 
0.2%
34
 
0.5%
212
 
1.4%
122
 
2.5%
0830
95.1%

max_energy
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.184422
Minimum0
Maximum400
Zeros830
Zeros (%)95.1%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2025-11-14T16:27:08.367408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum400
Range400
Interquartile range (IQR)0

Descriptive statistics

Standard deviation60.043441
Coefficient of variation (CV)4.5541202
Kurtosis21.077352
Mean13.184422
Median Absolute Deviation (MAD)0
Skewness4.6530642
Sum11510
Variance3605.2148
MonotonicityNot monotonic
2025-11-14T16:27:08.454042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0830
95.1%
20020
 
2.3%
36012
 
1.4%
2707
 
0.8%
3003
 
0.3%
4001
 
0.1%
ValueCountFrequency (%)
0830
95.1%
20020
 
2.3%
2707
 
0.8%
3003
 
0.3%
36012
 
1.4%
4001
 
0.1%
ValueCountFrequency (%)
4001
 
0.1%
36012
 
1.4%
3003
 
0.3%
2707
 
0.8%
20020
 
2.3%
0830
95.1%

shockable_rhythm
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
861 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0861
98.6%
112
 
1.4%

Length

2025-11-14T16:27:08.556369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:08.615962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0861
98.6%
112
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0861
98.6%
112
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0861
98.6%
112
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0861
98.6%
112
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0861
98.6%
112
 
1.4%

rosc
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
1
481 
0
392 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1481
55.1%
0392
44.9%

Length

2025-11-14T16:27:08.690100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:08.753029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1481
55.1%
0392
44.9%

Most occurring characters

ValueCountFrequency (%)
1481
55.1%
0392
44.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1481
55.1%
0392
44.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1481
55.1%
0392
44.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1481
55.1%
0392
44.9%

survival_24h
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
684 
1
187 
-1
 
2

Length

Max length2
Median length1
Mean length1.002291
Min length1

Characters and Unicode

Total characters875
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0684
78.4%
1187
 
21.4%
-12
 
0.2%

Length

2025-11-14T16:27:08.844977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:08.912509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0684
78.4%
1189
 
21.6%

Most occurring characters

ValueCountFrequency (%)
0684
78.2%
1189
 
21.6%
-2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0684
78.2%
1189
 
21.6%
-2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0684
78.2%
1189
 
21.6%
-2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0684
78.2%
1189
 
21.6%
-2
 
0.2%

survival_to_discharge
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
823 
1
 
49
-1
 
1

Length

Max length2
Median length1
Mean length1.0011455
Min length1

Characters and Unicode

Total characters874
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0823
94.3%
149
 
5.6%
-11
 
0.1%

Length

2025-11-14T16:27:09.004443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:09.070605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0823
94.3%
150
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0823
94.2%
150
 
5.7%
-1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0823
94.2%
150
 
5.7%
-1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0823
94.2%
150
 
5.7%
-1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0823
94.2%
150
 
5.7%
-1
 
0.1%

cpr_duration_min
Real number (ℝ)

Missing  Zeros 

Distinct204
Distinct (%)25.4%
Missing71
Missing (%)8.1%
Infinite0
Infinite (%)0.0%
Mean38952.296
Minimum0
Maximum466614
Zeros21
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2025-11-14T16:27:09.167850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median13
Q331.75
95-th percentile295207
Maximum466614
Range466614
Interquartile range (IQR)25.75

Descriptive statistics

Standard deviation95046.192
Coefficient of variation (CV)2.4400665
Kurtosis6.5865878
Mean38952.296
Median Absolute Deviation (MAD)8
Skewness2.6898374
Sum31239741
Variance9.0337787 × 109
MonotonicityNot monotonic
2025-11-14T16:27:09.302182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
552
 
6.0%
1043
 
4.9%
835
 
4.0%
634
 
3.9%
432
 
3.7%
232
 
3.7%
1531
 
3.6%
1130
 
3.4%
1226
 
3.0%
725
 
2.9%
Other values (194)462
52.9%
(Missing)71
 
8.1%
ValueCountFrequency (%)
021
2.4%
18
 
0.9%
232
3.7%
325
2.9%
432
3.7%
552
6.0%
634
3.9%
725
2.9%
835
4.0%
921
2.4%
ValueCountFrequency (%)
4666141
0.1%
4665731
0.1%
4651401
0.1%
4651301
0.1%
4651251
0.1%
4248131
0.1%
4248101
0.1%
4248071
0.1%
4233831
0.1%
4233701
0.1%

arrest_to_cpr_min
Real number (ℝ)

Skewed  Zeros 

Distinct6
Distinct (%)0.7%
Missing5
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean96.260369
Minimum0
Maximum83520
Zeros850
Zeros (%)97.4%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2025-11-14T16:27:09.409480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum83520
Range83520
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2834.8522
Coefficient of variation (CV)29.449837
Kurtosis867.99998
Mean96.260369
Median Absolute Deviation (MAD)0
Skewness29.461839
Sum83554
Variance8036387
MonotonicityNot monotonic
2025-11-14T16:27:09.485579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0850
97.4%
28
 
0.9%
15
 
0.6%
33
 
0.3%
835201
 
0.1%
41
 
0.1%
(Missing)5
 
0.6%
ValueCountFrequency (%)
0850
97.4%
15
 
0.6%
28
 
0.9%
33
 
0.3%
41
 
0.1%
835201
 
0.1%
ValueCountFrequency (%)
835201
 
0.1%
41
 
0.1%
33
 
0.3%
28
 
0.9%
15
 
0.6%
0850
97.4%

cpr_dt
Date

Distinct857
Distinct (%)98.7%
Missing5
Missing (%)0.6%
Memory size6.9 KiB
Minimum2018-04-05 15:00:00
Maximum2024-02-29 01:45:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-14T16:27:09.595361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:09.746679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct861
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
Minimum2018-04-05 15:00:00
Maximum2024-02-29 01:45:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-14T16:27:09.895396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:10.040246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

rosc_dt
Date

Missing 

Distinct793
Distinct (%)98.8%
Missing70
Missing (%)8.0%
Memory size6.9 KiB
Minimum2018-05-14 02:01:00
Maximum2024-11-02 23:16:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-14T16:27:10.174979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:10.322786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

rhythm_AF
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
870 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0870
99.7%
13
 
0.3%

Length

2025-11-14T16:27:10.446919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:10.512539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0870
99.7%
13
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0870
99.7%
13
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0870
99.7%
13
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0870
99.7%
13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0870
99.7%
13
 
0.3%

rhythm_Asystole
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
450 
1
423 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0450
51.5%
1423
48.5%

Length

2025-11-14T16:27:10.884507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:10.958640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0450
51.5%
1423
48.5%

Most occurring characters

ValueCountFrequency (%)
0450
51.5%
1423
48.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0450
51.5%
1423
48.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0450
51.5%
1423
48.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0450
51.5%
1423
48.5%

rhythm_Bradycardia
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
791 
1
82 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0791
90.6%
182
 
9.4%

Length

2025-11-14T16:27:11.037996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:11.096771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0791
90.6%
182
 
9.4%

Most occurring characters

ValueCountFrequency (%)
0791
90.6%
182
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0791
90.6%
182
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0791
90.6%
182
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0791
90.6%
182
 
9.4%

rhythm_PEA
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
866 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0866
99.2%
17
 
0.8%

Length

2025-11-14T16:27:11.171104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:11.238488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0866
99.2%
17
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0866
99.2%
17
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0866
99.2%
17
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0866
99.2%
17
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0866
99.2%
17
 
0.8%

rhythm_Sinus/Other
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
862 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0862
98.7%
111
 
1.3%

Length

2025-11-14T16:27:11.315696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:11.380393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0862
98.7%
111
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0862
98.7%
111
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0862
98.7%
111
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0862
98.7%
111
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0862
98.7%
111
 
1.3%

rhythm_VF
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
865 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0865
99.1%
18
 
0.9%

Length

2025-11-14T16:27:11.456094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:11.516387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0865
99.1%
18
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0865
99.1%
18
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0865
99.1%
18
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0865
99.1%
18
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0865
99.1%
18
 
0.9%

rhythm_VT
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
869 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0869
99.5%
14
 
0.5%

Length

2025-11-14T16:27:11.591120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:11.650647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0869
99.5%
14
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0869
99.5%
14
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0869
99.5%
14
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0869
99.5%
14
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0869
99.5%
14
 
0.5%

rhythm_nan
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
538 
1
335 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0538
61.6%
1335
38.4%

Length

2025-11-14T16:27:11.723481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:11.796085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0538
61.6%
1335
38.4%

Most occurring characters

ValueCountFrequency (%)
0538
61.6%
1335
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0538
61.6%
1335
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0538
61.6%
1335
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0538
61.6%
1335
38.4%

loc_CCU
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
851 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0851
97.5%
122
 
2.5%

Length

2025-11-14T16:27:11.924927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:12.027830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0851
97.5%
122
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0851
97.5%
122
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0851
97.5%
122
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0851
97.5%
122
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0851
97.5%
122
 
2.5%

loc_COVID_WARD
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
863 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0863
98.9%
110
 
1.1%

Length

2025-11-14T16:27:12.131955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:12.219515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0863
98.9%
110
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0863
98.9%
110
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0863
98.9%
110
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0863
98.9%
110
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0863
98.9%
110
 
1.1%

loc_CVU
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
811 
1
 
62

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0811
92.9%
162
 
7.1%

Length

2025-11-14T16:27:12.330704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:12.423905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0811
92.9%
162
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0811
92.9%
162
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0811
92.9%
162
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0811
92.9%
162
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0811
92.9%
162
 
7.1%

loc_DSU
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
863 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0863
98.9%
110
 
1.1%

Length

2025-11-14T16:27:12.530237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:12.620367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0863
98.9%
110
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0863
98.9%
110
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0863
98.9%
110
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0863
98.9%
110
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0863
98.9%
110
 
1.1%

loc_FMS
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
838 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0838
96.0%
135
 
4.0%

Length

2025-11-14T16:27:12.731816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:12.819629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0838
96.0%
135
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0838
96.0%
135
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0838
96.0%
135
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0838
96.0%
135
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0838
96.0%
135
 
4.0%

loc_ICU
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
1
640 
0
233 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1640
73.3%
0233
 
26.7%

Length

2025-11-14T16:27:12.923840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:13.030838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1640
73.3%
0233
 
26.7%

Most occurring characters

ValueCountFrequency (%)
1640
73.3%
0233
 
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1640
73.3%
0233
 
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1640
73.3%
0233
 
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1640
73.3%
0233
 
26.7%

loc_MMS
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
804 
1
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0804
92.1%
169
 
7.9%

Length

2025-11-14T16:27:13.150533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:13.235822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0804
92.1%
169
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0804
92.1%
169
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0804
92.1%
169
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0804
92.1%
169
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0804
92.1%
169
 
7.9%

loc_NEONATAL_ICU
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
872 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

Length

2025-11-14T16:27:13.337245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:13.430602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

loc_OBSTETRICS
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
872 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

Length

2025-11-14T16:27:13.549084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:13.642092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

loc_OPERATING_ROOM
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
871 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0871
99.8%
12
 
0.2%

Length

2025-11-14T16:27:13.753671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:13.855404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0871
99.8%
12
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0871
99.8%
12
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0871
99.8%
12
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0871
99.8%
12
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0871
99.8%
12
 
0.2%

loc_PEDIATRIC
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
861 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0861
98.6%
112
 
1.4%

Length

2025-11-14T16:27:13.964368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:14.064964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0861
98.6%
112
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0861
98.6%
112
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0861
98.6%
112
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0861
98.6%
112
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0861
98.6%
112
 
1.4%

loc_PEDIATRIC_ICU
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
871 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0871
99.8%
12
 
0.2%

Length

2025-11-14T16:27:14.195753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:14.280905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0871
99.8%
12
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0871
99.8%
12
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0871
99.8%
12
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0871
99.8%
12
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0871
99.8%
12
 
0.2%

loc_POST_ICU
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
867 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0867
99.3%
16
 
0.7%

Length

2025-11-14T16:27:14.353582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:14.414449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0867
99.3%
16
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0867
99.3%
16
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0867
99.3%
16
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0867
99.3%
16
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0867
99.3%
16
 
0.7%

loc_UNKNOWN
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
0
872 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters873
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

Length

2025-11-14T16:27:14.485283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T16:27:14.542665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0872
99.9%
11
 
0.1%

Interactions

2025-11-14T16:27:04.739987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:01.944795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:02.480409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:02.990185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:04.178358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:04.849350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:02.049865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:02.573823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:03.097054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:04.277673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:04.959204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:02.152345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:02.686977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:03.840293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:04.379924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:05.069073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:02.259289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:02.797023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:03.954144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:04.498959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:05.174723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:02.371891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:02.893772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:04.065959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T16:27:04.609240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-14T16:27:14.661068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
agearrest_to_cpr_mincancercopdcoronary_artery_diseasecovid_on_admissioncpr_duration_mindiabetesgenderheart_diseaseheart_failurehypertensionloc_CCUloc_COVID_WARDloc_CVUloc_DSUloc_FMSloc_ICUloc_MMSloc_NEONATAL_ICUloc_OBSTETRICSloc_OPERATING_ROOMloc_PEDIATRICloc_PEDIATRIC_ICUloc_POST_ICUloc_UNKNOWNmax_energyrhythm_AFrhythm_Asystolerhythm_Bradycardiarhythm_PEArhythm_Sinus/Otherrhythm_VFrhythm_VTrhythm_nanroscshock_countshockable_rhythmsmokingsurvival_24hsurvival_to_discharge
age1.000-0.0080.2040.0770.0000.123-0.0010.2510.0870.3430.1690.3310.0380.0900.0000.0920.0150.0700.1350.1640.0000.0000.0760.2540.0770.164-0.0100.0000.0000.0000.0000.0600.0760.0000.0000.076-0.0120.0000.1770.1450.228
arrest_to_cpr_min-0.0081.0000.0110.0000.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0780.0000.0000.0330.0000.0000.0000.0000.0000.0000.0780.0000.0000.0450.000
cancer0.2040.0111.0000.0890.0500.0460.0310.1010.0000.1120.0810.1080.0420.0000.0210.0000.0290.0000.0120.0000.0000.0000.0400.0000.0000.0000.0000.0000.0400.0000.0000.0000.0000.0000.0420.0510.0000.0000.0590.0580.030
copd0.0770.0000.0891.0000.0000.0640.0750.0000.0000.0990.0000.0690.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.1280.0000.0610.0000.000
coronary_artery_disease0.0000.0000.0500.0001.0000.0000.0000.1440.0000.1440.0000.0000.0960.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0590.0000.032
covid_on_admission0.1230.0000.0460.0640.0001.0000.0000.0000.0000.0290.0500.0000.0000.3580.0610.0640.0000.2960.3770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0000.0000.0000.0740.1230.0000.0980.0580.018
cpr_duration_min-0.0010.0570.0310.0750.0000.0001.0000.0000.0000.0760.0610.0000.0000.0000.0000.0470.0000.0000.0510.0000.0000.2670.0000.0000.0000.0000.1100.0000.0000.1250.0460.0330.1190.0710.0710.0730.1110.0950.0110.0000.090
diabetes0.2510.0000.1010.0000.1440.0000.0001.0000.0180.2650.1020.2630.0920.0000.1270.0000.0310.0330.0000.0000.0000.0000.0730.0000.0000.0000.0180.0000.0000.0000.0000.2960.0000.0000.0000.0700.0000.0000.2020.0310.089
gender0.0870.0000.0000.0000.0000.0000.0000.0181.0000.0690.0000.0970.0000.0000.0180.0000.1430.0000.0890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0460.0000.0000.0000.0000.1060.0200.000
heart_disease0.3430.0000.1120.0990.1440.0290.0760.2650.0691.0000.4360.3630.0510.0000.0240.0000.0000.0000.0420.0000.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0860.0640.036
heart_failure0.1690.0000.0810.0000.0000.0500.0610.1020.0000.4361.0000.1850.1070.0000.0000.0000.0150.0990.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0720.0000.0160.0000.000
hypertension0.3310.0000.1080.0690.0000.0000.0000.2630.0970.3630.1851.0000.0220.0000.0780.0000.0000.0000.0000.0000.0000.0000.0740.0370.0000.0000.0000.0000.0000.0000.0000.0000.3490.0000.0000.0400.0000.2830.0310.0410.092
loc_CCU0.0380.0000.0420.0000.0960.0000.0000.0920.0000.0510.1070.0221.0000.0000.0000.0000.0000.2560.0000.0000.0000.0000.0000.0000.0000.0000.0980.0000.0000.0200.0000.0000.0000.0000.0000.0360.0740.0000.0900.0000.000
loc_COVID_WARD0.0900.0000.0000.0000.0000.3580.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.1630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1670.0000.000
loc_CVU0.0000.0000.0210.0000.0000.0610.0000.1270.0180.0240.0000.0780.0000.0001.0000.0000.0300.4520.0640.0000.0000.0000.0000.0000.0000.0000.0660.0000.0360.0000.0000.0000.0000.0000.0030.0190.1160.0000.0000.0560.158
loc_DSU0.0920.0000.0000.0000.0000.0640.0470.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.1630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1280.0000.0000.0000.0270.0000.0000.0370.0100.000
loc_FMS0.0150.0000.0290.0000.0000.0000.0000.0310.1430.0000.0150.0000.0000.0000.0300.0001.0000.3310.0360.0000.0000.0000.0000.0000.0000.0000.0620.0000.0400.0000.0000.0000.0000.0000.0510.0000.0000.0000.0040.0000.000
loc_ICU0.0700.0000.0000.0000.0370.2960.0000.0330.0000.0000.0990.0000.2560.1630.4520.1630.3311.0000.4800.0000.0000.0400.1820.0400.1170.0000.0620.0000.0270.0000.0210.0130.0000.0000.0610.0000.0590.0170.0340.0220.090
loc_MMS0.1350.0000.0120.0000.0000.3770.0510.0000.0890.0420.0000.0000.0000.0000.0640.0000.0360.4801.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0920.0720.0000.0000.0000.000
loc_NEONATAL_ICU0.1640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.000
loc_OBSTETRICS0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
loc_OPERATING_ROOM0.0000.0000.0000.0000.0000.0000.2670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.000
loc_PEDIATRIC0.0760.0000.0400.0000.0000.0000.0000.0730.0000.0290.0000.0740.0000.0000.0000.0000.0000.1820.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0450.0000.000
loc_PEDIATRIC_ICU0.2540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0400.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.000
loc_POST_ICU0.0770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1170.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.000
loc_UNKNOWN0.1640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.000
max_energy-0.0100.0780.0000.0000.0000.0000.1100.0180.0000.0000.0000.0000.0980.0000.0660.0000.0620.0620.0360.0000.0000.0000.0000.0000.0000.0001.0000.0000.0400.0000.0670.0000.0860.4940.0320.0000.9990.2910.0000.0000.085
rhythm_AF0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.000
rhythm_Asystole0.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0400.0270.0000.0000.0000.0000.0000.0000.0000.0000.0400.0161.0000.3070.0660.0930.0740.0350.7620.1600.0320.0990.0000.1470.112
rhythm_Bradycardia0.0000.0330.0000.0000.0000.0560.1250.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3071.0000.0000.0000.0000.0000.2480.0740.0000.0000.0000.0660.000
rhythm_PEA0.0000.0000.0000.0000.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0670.0000.0660.0001.0000.0000.0000.0000.0470.0260.3660.0000.0000.0990.000
rhythm_Sinus/Other0.0600.0000.0000.0000.0000.0000.0330.2960.0110.0090.0000.0000.0000.0000.0000.1280.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0930.0000.0001.0000.0000.0000.0710.0370.0000.0000.0660.0780.039
rhythm_VF0.0760.0000.0000.0000.0000.0000.1190.0000.0000.0000.0000.3490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0860.0000.0740.0000.0000.0001.0000.0000.0540.0000.1740.7630.0000.0840.124
rhythm_VT0.0000.0000.0000.0000.0000.0000.0710.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4940.0000.0350.0000.0000.0000.0001.0000.0130.0280.1050.5010.0000.0750.031
rhythm_nan0.0000.0000.0420.0000.0000.0000.0710.0000.0000.0000.0000.0000.0000.0000.0030.0000.0510.0610.0000.0000.0000.0120.0000.0120.0040.0000.0320.0000.7620.2480.0470.0710.0540.0131.0000.0620.0000.0760.0000.0000.049
rosc0.0760.0000.0510.0370.0000.0740.0730.0700.0000.0000.0000.0400.0360.0000.0190.0270.0000.0000.0920.0000.0000.0000.0240.0000.0000.0000.0000.0000.1600.0740.0260.0370.0000.0280.0621.0000.0000.0460.0290.4720.217
shock_count-0.0120.0780.0000.1280.0000.1230.1110.0000.0000.0000.0720.0000.0740.0000.1160.0000.0000.0590.0720.0000.0000.0000.0000.0000.0000.0000.9990.0000.0320.0000.3660.0000.1740.1050.0000.0001.0000.2230.1240.0180.000
shockable_rhythm0.0000.0000.0000.0000.0000.0000.0950.0000.0000.0000.0000.2830.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.2910.0000.0990.0000.0000.0000.7630.5010.0760.0460.2231.0000.0000.1210.134
smoking0.1770.0000.0590.0610.0590.0980.0110.2020.1060.0860.0160.0310.0900.1670.0000.0370.0040.0340.0000.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0660.0000.0000.0000.0290.1240.0001.0000.0380.038
survival_24h0.1450.0450.0580.0000.0000.0580.0000.0310.0200.0640.0000.0410.0000.0000.0560.0100.0000.0220.0000.0440.0000.0000.0000.0000.0000.0440.0000.0440.1470.0660.0990.0780.0840.0750.0000.4720.0180.1210.0381.0000.598
survival_to_discharge0.2280.0000.0300.0000.0320.0180.0900.0890.0000.0360.0000.0920.0000.0000.1580.0000.0000.0900.0000.0000.0000.0000.0000.0000.0000.0000.0850.0000.1120.0000.0000.0390.1240.0310.0490.2170.0000.1340.0380.5981.000

Missing values

2025-11-14T16:27:05.427366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-14T16:27:05.787614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-14T16:27:06.037432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

agegendersmokingcoronary_artery_diseaseheart_failureheart_diseasehypertensioncopddiabetescancercovid_on_admissionshock_countmax_energyshockable_rhythmroscsurvival_24hsurvival_to_dischargecpr_duration_minarrest_to_cpr_mincpr_dtarrest_dtrosc_dtrhythm_AFrhythm_Asystolerhythm_Bradycardiarhythm_PEArhythm_Sinus/Otherrhythm_VFrhythm_VTrhythm_nanloc_CCUloc_COVID_WARDloc_CVUloc_DSUloc_FMSloc_ICUloc_MMSloc_NEONATAL_ICUloc_OBSTETRICSloc_OPERATING_ROOMloc_PEDIATRICloc_PEDIATRIC_ICUloc_POST_ICUloc_UNKNOWN
051.0110001000000011115.00.02018-06-26 15:15:002018-06-26 15:15:002018-06-26 15:30:000100000000000010000000
155.0010000000000000038.02.02018-07-14 14:32:002018-07-14 14:30:002018-07-14 15:10:000100000000100000000000
253.000000100100000000.00.02018-07-30 15:30:002018-07-30 15:30:002018-07-30 15:30:000000000100001000000000
378.00000010100000111NaN0.02018-10-14 14:00:002018-10-14 14:00:00NaT0000000100100000000000
474.01000010100000100213135.00.02018-06-11 04:15:002018-06-11 04:15:002018-11-06 04:30:000100000000000010000000
543.0100000000000000024.00.02018-11-22 20:37:002018-11-22 20:37:002018-11-22 21:01:000100000000000010000000
659.0000000001000010041772.00.02018-09-10 12:35:002018-09-10 12:35:002018-10-09 12:47:000100000000001000000000
789.0100011000000000016.00.02018-11-26 06:01:002018-11-26 06:01:002018-11-26 06:17:000100000000100000000000
830.0000000001000000012.00.02018-06-17 10:44:002018-06-17 10:44:002018-06-17 10:56:000000000100001000000000
970.0000011000000000048.00.02018-08-28 18:12:002018-08-28 18:12:002018-08-28 19:00:000000100000100000000000
agegendersmokingcoronary_artery_diseaseheart_failureheart_diseasehypertensioncopddiabetescancercovid_on_admissionshock_countmax_energyshockable_rhythmroscsurvival_24hsurvival_to_dischargecpr_duration_minarrest_to_cpr_mincpr_dtarrest_dtrosc_dtrhythm_AFrhythm_Asystolerhythm_Bradycardiarhythm_PEArhythm_Sinus/Otherrhythm_VFrhythm_VTrhythm_nanloc_CCUloc_COVID_WARDloc_CVUloc_DSUloc_FMSloc_ICUloc_MMSloc_NEONATAL_ICUloc_OBSTETRICSloc_OPERATING_ROOMloc_PEDIATRICloc_PEDIATRIC_ICUloc_POST_ICUloc_UNKNOWN
86384.00000000000000000296646.00.02024-02-09 07:38:002024-02-09 07:38:002024-09-02 07:44:000000000100000100000000
86486.01100111000000000338406.00.02024-02-10 11:12:002024-02-10 11:12:002024-10-02 11:18:000100000000000100000000
86570.010011101100000007.00.02024-02-13 14:37:002024-02-13 14:37:002024-02-13 14:44:000100000000000100000000
86682.010001101000001002.00.02024-02-15 03:24:002024-02-15 03:24:002024-02-15 03:26:000100000000000100000000
86765.0110111011000011015.00.02024-02-16 06:00:002024-02-16 06:00:002024-02-16 06:15:000010000000000100000000
86874.0110110100000000021.00.02024-02-16 20:13:002024-02-16 20:13:002024-02-16 20:34:000100000010000000000000
86957.010000000000001003.00.02024-02-23 15:35:002024-02-23 15:35:002024-02-23 15:38:000000000100000100000000
87067.0110000011000000018.00.02024-02-27 10:02:002024-02-27 10:02:002024-02-27 10:20:000000000100000100000000
87170.000000101000001006.00.02024-02-27 19:15:002024-02-27 19:15:002024-02-27 19:21:000000000100000100000000
87264.0110111000000010027.00.02024-02-29 01:45:002024-02-29 01:45:002024-02-29 02:12:000010000000000100000000

Duplicate rows

Most frequently occurring

agegendersmokingcoronary_artery_diseaseheart_failureheart_diseasehypertensioncopddiabetescancercovid_on_admissionshock_countmax_energyshockable_rhythmroscsurvival_24hsurvival_to_dischargecpr_duration_minarrest_to_cpr_mincpr_dtarrest_dtrosc_dtrhythm_AFrhythm_Asystolerhythm_Bradycardiarhythm_PEArhythm_Sinus/Otherrhythm_VFrhythm_VTrhythm_nanloc_CCUloc_COVID_WARDloc_CVUloc_DSUloc_FMSloc_ICUloc_MMSloc_NEONATAL_ICUloc_OBSTETRICSloc_OPERATING_ROOMloc_PEDIATRICloc_PEDIATRIC_ICUloc_POST_ICUloc_UNKNOWN# duplicates
02.0100000000000011010.00.02020-07-18 07:00:002020-07-18 07:00:002020-07-18 07:10:0000000001000001000000002
191.010000001000001106.00.02023-03-22 15:50:002023-03-22 15:50:002023-03-22 15:56:0001000000100000000000002